Algorithmic Finance

We introduce an event based framework mapping financial data onto a state based discretisation of time series. The mapping is intrinsically multi-scale and naturally accommodates itself with tick-by-tick data. Within this framework, we define an information theoretic quantity that characterises the unlikeliness of price trajectories and, akin to a liquidity measure, detects and predicts stress in financial markets. In particular, we show empirical examples within the foreign exchange market where the new measure not only quantifies liquidity but also seems to act as an early warning signal.

Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain’s heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction.

We develop a model to study the role of individual rationality in economics and biology. The model’s agents differ continuously in their ability to make rational choices. The agents’ objective is to ensure their individual survival over time or, equivalently, to maximize profits. In equilibrium, however, individually rational agents who maximize their objective survival probability are, individually and collectively, eliminated by the forces of competition. Instead of individual rationality, there emerges a unique distribution of irrational players who are individually not fit for the struggle of survival. The selection of irrational players over rational ones relies on the fact that all rational players coordinate on the same optimal action, which leaves them collectively undiversified and thus vulnerable to aggregate risks.

In this paper we introduce natural time analysis in financial markets. Due to the remarkable results of this analysis on earthquake prediction and the similarities of earthquake data to financial time series, its application in price prediction and algorithmic trading seems to be a natural choice. This is tested through a trading strategy with very encouraging results.